I hold a PhD in physics from the University of Cambridge, and have significant R&D experience across academia and industry. Over the last ten years, I have been working in the space sector, applying my optical expertise in the design and manufacture of space-based telescopes, as well as specializing in Python-based image analysis and software development. I am a well-known authority in machine and deep learning techniques for processing satellite and aerial imagery, and am dedicated to education. I have created the satellite-image-deep-learning.com website, newsletter, YouTube channel, and Github organization to share my knowledge and build a community. As a strong proponent of the open-source software movement, I regularly contribute to Github and strive to make a positive impact in the developer community. I have had the opportunity to share my expertise by presenting at various Python conferences and have been invited as a guest on several podcasts, including the ZenML and Mapscaping podcasts. Through these opportunities, I am able to share my knowledge and passion for the industry, while also connecting with other like-minded individuals.
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Tackling the worlds toughest challenges with AI & ML applied to satellite imagery
- London, UK
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09:27
(UTC -12:00) - @robmarkcole
- in/robmarkcole
- @satellite-image-deep-learning
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satellite-image-deep-learning/techniques
satellite-image-deep-learning/techniques PublicTechniques for deep learning with satellite & aerial imagery
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fire-detection-from-images
fire-detection-from-images PublicDetect fire in images using neural nets
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mqtt-camera-streamer
mqtt-camera-streamer PublicStream images from a connected camera over MQTT, view using Streamlit, record to file and sqlite
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yolov5-flask
yolov5-flask Public archiveOfficial implementation at https://github.com/ultralytics/yolov5/tree/master/utils/flask_rest_api
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HASS-plate-recognizer
HASS-plate-recognizer PublicRead number plates with https://platerecognizer.com/
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coral-pi-rest-server
coral-pi-rest-server PublicPerform inferencing of tensorflow-lite models on an RPi with acceleration from Coral USB stick
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